In-Situ Unmanned Aerial Vehicle Sensor Calibration to Improve Automatic Image Orthorectification

Small, low-altitude unmanned aerial vehicles (UAV)s can be
very useful in many ecological applications as a personal remote
sensing platform. However, in many cases it is difficult
to produce a single georeferenced mosaic from the many
small images taken from the UAV. This is due to the lack of
features in the images and the inherent errors from the inexpensive
navigation sensors. This paper focuses on improving
the orthorectification accuracy by finding these errors and
calibrating the navigation sensors. This is done by inverseorthorectifying
a set of images collected during flight using
ground targets and General Procrustes Analysis. By comparing
the calculated data from the inverse-orthorectification
and the measured data from the navigation sensors, different
sources of errors can be found and characterized, such as GPS
computational delay, logging delay, and biases. With this
method, the orthorectification errors are reduced from less
than 60m to less than 1.5m.